The explosion of information available over network-based systems, such as the Internet can overwhelm a person attempting to locate a desired piece of information or product. For example, a person looking for a place to eat or a local entertainment event can search the Internet, but is likely to either be overwhelmed by the volume of data or unable to locate any relevant information. The dramatic growth in the amount of information available via the Internet has left users with the problem of sorting and browsing through enormous amounts of data to find information relevant to their needs. Targeted search services and recommendation engines have been implemented to attempt to assist users in locating relevant information or products. A successful recommendation system not only saves users time in locating relevant information (e.g., a place to eat or product to purchase) but can also drive extra profits through advertising or additional sales revenue for the service provider operating the search service or recommendation engine.
The advent of Internet connected smartphones, such as the iPhone™ (from Apple, Inc. of Cupertino, Calif.), has compounded the problem by making the vast amounts of information available via the Internet available anywhere. Smartphones also typically have location determination capabilities, adding to the available data for use by a search or recommendation engine. However, providing real-time location-aware recommendations presents significant challenges.
Traditional recommendation systems use some form of collaborative filtering to reduce the volume of information found through simple keyword or natural language searching. Two different basic types of collaborative filtering are typically employed by recommendation systems, user-based or item-based (or target-based). User-based collaborative filtering focuses on grouping like user behavior. Item-based recommendation systems focus on grouping similar items. Item-based recommendations using collaborative filtering can be used by commerce sites to provide recommendations based on the purchase history of users who bought similar products (e.g., users who bought X also bought Y and Z). Item-based recommendations can also be applied to things like restaurants or entertainment venues. However, collaborative filtering is limited, especially when location is added to the recommendation matrix.